Triple
T4258032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirsty Sword-Gusmão |
E96028
|
entity |
| Predicate | notableOccupation |
P47271
|
FINISHED |
| Object | First Lady |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: First Lady | Statement: [Kirsty Sword-Gusmão, notableOccupation, First Lady]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableOccupation Context triple: [Kirsty Sword-Gusmão, notableOccupation, First Lady]
-
A.
notableHolderOccupation
chosen
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional role.
-
B.
notableOccupationContext
Indicates that the referenced occupation is notable or significant specifically within the given contextual framework or domain.
-
C.
notableWorkRole
Indicates that a person’s role or position is specifically associated with the creation, performance, or contribution to a notable work.
-
D.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
E.
notableTypeOfWork
Indicates that a work is a significant or defining example within a particular type or category of work associated with an entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b3454095ac81909c2494f7ff294af1 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34f7ec4508190a5067f1112ac7dca |
completed | March 12, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69b347f73e008190a908a48ef389945a |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:06 p.m.